Harigeeta: Cic Mechanism with Euclidean Steiner Tree for Service Latency Prediction in Delay-Sensitive Cloud Services
Abstract Data establishment and resource provision are the most crucial tasks in the data center. To achieve minimum service latency, it is required to have a balance between the virtual machine and physical machine for proper execution of any query into the cloud data center. Cloud services have a huge market in the world trade. These services have a large impact on every field, also on research. Latency is a major problem in the growth of the cloud market in a real time scenario. Online trade, marketing and banking have a large market of cloud services, which require minimum latency in the real-time response otherwise the whole market would be destroyed. Latency prediction plays a crucial role in managing the load on the data center. To perfectly maintain a request waiting queue, it is required to predict accurate latency between the virtual machines in the data center. If any approach can predict accurate latency in the data center for any particular request, then it can perfectly manage the waiting queue for the cloud data center. Thus, prediction plays a crucial role in reducing latency in the execution of any request to the cloud data center. This article presents an online latency prediction approach for VMs to improve load balancing. A Euclidean Circle Steiner Tree point is proposed. Results show compression with existing mechanisms and get 8-12 % more accuracy in latency prediction.
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Amaral, D. M., Gondim, J. J. C., Albuquerque, R. D. O., Orozco, A. L. S., & Villalba, L. J. G. (2019). Hy-sail: Hyper-scalability, availability and integrity layer for cloud storage systems. IEEE Access, 7, 90082–90093. 10.1109/ACCESS.2019.2925735
Ardagna, D., (2015). Cloud and multi-cloud computing: Current challenges and future applications. IEEE/ACM 7th International Workshop on Primary Engineering Service-Oriented and Cloud Systems, 10.1109/PESOS.2015.8 (Workshop)
Asghari, A., Sohrabi, M. K., & Yaghmaee, F. (2020). Online scheduling of dependent tasks of cloud ‘s workflows to enhance resource utilization and reduce the makespan using multiple reinforcement learning-based agents. Soft Computing, 24(21), 16177–16199.
Balasubramanian, V., Zaman, F., Aloqaily, M., Alrabaee, S., & Reisslein, M. (2019). Reinforcing the edge: Autonomous energy management for mobile device clouds. In IEEE International Conference on Computer Communications
Cao, R., Tang, Z., Li, K., & Li, K. (2018). Hmgowm: A hybrid decision mechanism for automating migration of virtual machines. IEEE Trans. on Services Computing. 10.1109/TSC.2018.2873694
Chakravarthi, K., Shyamala, L., & V., V. (2020). Budget aware scheduling algorithm for workflow applications in iaas clouds. Cluster Computing.
Chen, K., Lei, C., Tseng, P., Chang, Y., & Huang, C., (2021). Measuring the latency of cloud gaming systems. 19th ACM International Conference on. Multimedia, pp. 1269–1272.
Cho, D., Taheri, J., Zomaya, A. Y., & Bouvry, P. (2017). Real-time virtual network function (vnf) migration toward low network latency in cloud environments. In 2017 IEEE 10th International Conference on Cloud Computing (CLOUD). 10.1109/CLOUD.2017.118
Harzog, B., (2020). Infrastructure Performance Management for Virtualized Systems. White Paper APM Experts, pp. 1-18.
Hemalata, & Singh, A., (2012). Compress analysis on low latency on different bandwidth and geographical location while using cloud-based applications. IJAET, ISSN: 2231-1963
Hu, X., & Du, D. (2018). Steiner Tree Problems in Computer Communication Networks, World Scientific Publishing Company.
Karuppiah, K. E., Lim, B. P., Yassin, Y. M., Chong, P. K., & Noor, M. F., Nazir, B. A., 2013. FARCREST: Euclidean Steiner Tree-based Cloud Service Latency Prediction System. 10th annual IEEE CCNC.
Lepakshi, V. A., & Prashanth, C. S. R. (2020). Efficient resource allocation with score for reliable task scheduling in cloud computing systems. 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA) (pp. 6–12). 10.1109/ICIMIA48430.2020.9074914
Li, P. (2017). Enabling low degraded read latency and fast recovery for erasure coded cloud storage systems. 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W). 10.1109/DSN-W.2017.27
Li, C., Zhang, Y., Hao, Z., & Luo, Y. (2020). An effective scheduling strategy based on hypergraph partition in geographically distributed datacenters. Computer Networks, 170, 107096.
Liang, Y.-C., Hung, S.-C., Lien, S.-Y., Chen, K.-C., (2015). Ultra-low-latency ubiquitous connections in heterogeneous cloud radio access networks, IEEE Wireless Communication., vol. 22, no. 3, pp. 22_31
Liu, G., & Shen, H. (2016). Minimum-cost cloud storage service across multiple cloud providers. IEEE 36th International Conference on Distributed Computing Systems (ICDCS). 10.1109/ICDCS.2016.36
Machida, F., Trivedi, K. S., & Kim D. S, (2015). Modeling and analysis of software rejuvenation in a server virtualized system. Perform. Eval., vol. 70, no. 3, pp. 212_230.
Malkhi, D., Kuhn, F., Ramasubramanian, V., Balakrishnan, M., Akella, A., Gupta, A., (2009). On the treeness of internet latency and bandwidth. ACM SIGMETRICS
Mao, B., Wu, S., & Jiang, H. (2016). Exploiting workload characteristics and service diversity to improve the availability of cloud storage systems. IEEE Transactions on Parallel and Distributed Systems, 27, 2010–2021. 10.1109/TPDS.2015.2475273
Medara, R., & Singh, R.S., (2022). A Review on Energy-Aware Scheduling Techniques for Workflows in IaaS Clouds. Wireless Personal Communication, 125, pages1545–1584
Medara, R., Singh, R. S., & Amit. (2021). Energy-aware workflow task scheduling in clouds with virtual machine consolidation using discrete water wave optimization. Simulation Modelling Practice and Theory, 110, 102323.
Medara, R., Singh, R. S., Kumar, U. S., & Barfa, S. (2020). Energy efficient virtual machine consolidation using water wave optimization. IEEE Congress on Evolutionary Computation (CEC) (pp. 1–7). IEEE.
Miladinovic, I., Schefer-Wenzl, S., Burger, T., & Hirner, H., (2021). Multi-Access Edge Computing: An Overview and Latency Evaluation. 22nd IEEE International Conference on Industrial Technology (ICIT), Valencia, Spain, pp. 744-748, doi: 10.1109/ICIT46573.2021.9453495.
Mithila S. P., & Baumgartner, G., (2022). Latency-based Vector Scheduling of Many-task Applications for a Hybrid Cloud. IEEE 15th International Conference on Cloud Computing (CLOUD), Barcelona, Spain, pp. 257-262, doi: 10.1109/CLOUD55607.2022.00047.
Nathan, S., Kulkarni, P., & Bellur, U., (2015). Towards a comprehensive performance model of virtual machine live migration, Proc. 6th ACM Symp. Cloud Comput., Kohala Coast, HI, USA, pp. 288_301.
Ridhawi, I. A., Mostafa, N., Kotb, Y., Aloqaily, M., & Abualhaol, I., (2017). Data caching and selection in 5g networks using f2f communication. In 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)
Sharma, R. K., Singh, S. P., Kamal P., (2015). A Latency reduction mechanism for virtual machine resource Allocation in delay sensitive cloud services. ICGCIoT.
Sharma, R. K., Singh, S. P., Kamal P., (2015). An Evaluation on Latency & its Measurement in cloud computing. INBUSH.
Tordsson, J., Monterob, R.S., Moreno-Vozmedianob, R., & Llorente, I.M., 2012. Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers. Future Generation Computer Systems, Vol. 28, pp. 358–367.
Veith, A.D.S., Assunção, M. D. D., & Lefèvre, L., (2023). Latency-Aware Strategies for Deploying Data Stream Processing Applications on Large Cloud-Edge Infrastructure. IEEE Transactions on Cloud Computing, vol. 11, no. 1, pp. 445-456, doi: 10.1109/TCC.2021.3097879.
Sharma, R. K., & Singh, S. (2024). Harigeeta: Cic Mechanism with Euclidean Steiner Tree for Service Latency Prediction in Delay-Sensitive Cloud Services. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 13(1), e31594. https://doi.org/10.14201/adcaij.31594
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